Natural Language Processing
Natural Language Processing (NLP) focuses on enabling computers to understand, interpret, and generate human language. Current research heavily emphasizes large language models (LLMs), exploring their capabilities in various tasks like question answering, text classification, and translation, while also addressing challenges such as bias, efficiency, and the need for better evaluation metrics. The field's significance lies in its potential to revolutionize numerous applications, from improving healthcare and education to enhancing information access and facilitating more effective human-computer interaction.
Papers
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, Xing Sun
Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding
Balaji Muralidharan, Hayden Beadles, Reza Marzban, Kalyan Sashank Mupparaju
Prompt Recursive Search: A Living Framework with Adaptive Growth in LLM Auto-Prompting
Xiangyu Zhao, Chengqian Ma
Reconsidering Degeneration of Token Embeddings with Definitions for Encoder-based Pre-trained Language Models
Ying Zhang, Dongyuan Li, Manabu Okumura
Misinforming LLMs: vulnerabilities, challenges and opportunities
Bo Zhou, Daniel Geißler, Paul Lukowicz
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs
Afia Anjum, Maksim E. Eren, Ismael Boureima, Boian Alexandrov, Manish Bhattarai
Automatic Generation of Behavioral Test Cases For Natural Language Processing Using Clustering and Prompting
Ying Li, Rahul Singh, Tarun Joshi, Agus Sudjianto
A Course Shared Task on Evaluating LLM Output for Clinical Questions
Yufang Hou, Thy Thy Tran, Doan Nam Long Vu, Yiwen Cao, Kai Li, Lukas Rohde, Iryna Gurevych
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
Riccardo Orlando, Pere-Lluis Huguet Cabot, Edoardo Barba, Roberto Navigli
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi, Jon Ander Campos, Yanai Elazar, Eneko Agirre, Yoav Goldberg, Wei-Lin Chen, Jenny Chim, Leshem Choshen, Luca D'Amico-Wong, Melissa Dell, Run-Ze Fan, Shahriar Golchin, Yucheng Li, Pengfei Liu, Bhavish Pahwa, Ameya Prabhu, Suryansh Sharma, Emily Silcock, Kateryna Solonko, David Stap, Mihai Surdeanu, Yu-Min Tseng, Vishaal Udandarao, Zengzhi Wang, Ruijie Xu, Jinglin Yang
ThinK: Thinner Key Cache by Query-Driven Pruning
Yuhui Xu, Zhanming Jie, Hanze Dong, Lei Wang, Xudong Lu, Aojun Zhou, Amrita Saha, Caiming Xiong, Doyen Sahoo
Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming
Yate Ge, Yi Dai, Run Shan, Kechun Li, Yuanda Hu, Xiaohua Sun
Can LLMs be Fooled? Investigating Vulnerabilities in LLMs
Sara Abdali, Jia He, CJ Barberan, Richard Anarfi
Sentiment Analysis of Lithuanian Online Reviews Using Large Language Models
Brigita Vileikytė, Mantas Lukoševičius, Lukas Stankevičius
Comparative Analysis of Encoder-Based NER and Large Language Models for Skill Extraction from Russian Job Vacancies
Nikita Matkin, Aleksei Smirnov, Mikhail Usanin, Egor Ivanov, Kirill Sobyanin, Sofiia Paklina, Petr Parshakov
Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks
Marco AF Pimentel, Clément Christophe, Tathagata Raha, Prateek Munjal, Praveen K Kanithi, Shadab Khan